Data Preparation

Model your data as a series of process observations or measures that are associated with an outcome of interest.  Compose each observation (row) as a common set of features (aka., independent variables or factors).  Both features and outcomes are either numerical (age, HbA1c, BP, etc.), binary (yes/no, true/false, etc.) or categorical (Gender, Service line , Floor unit, Shift, DRG, etc.).

Provide a CSV file with a header row labeling each column.

  1. The first column is an alphanumeric index starting with a letter that is used to uniquely identify the data row.
  2. Provide additional columns of either categorical data or numeric data.
  3. The first row is a user supplied alphanumeric header for each column

 Sample Table:

ROW_1 Red Hot Early
ROW_2 Yellow Cold Late
ROW_n Yellow Cold On-Time

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Try it now with your data.

Pattern Discovery and Prediction

  1. We engineer features, as needed, to remove noise and improve the quality of findings and predictive models.

  2. Using your domain knowledge we select the data columns of interest for processing

  3. We auto-tune the machine learning to your data so that turnaround times can be exceedingly fast.

Emailed Results Provide Early Insight

  1. You receive a list of the top anomalies rated by confidence

  2. You are provided with a chart that profiles factors associated with the top anomalies.  This allows for generation of simple process decisioning rules fostering implementation of realtime corrective process interventions.  

  3. We provide ranges of numerical values that are contributing factors.


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